The present invention relates to an information mining and categorization technology, and more specifically, to a method and apparatus for categorizing a use scenario of a product.
Generally, when purchasing a product, a person would like to know more use scenarios of the product to determine whether the product meets his/her own demand. However, a product specification provides very limited description about use scenarios of the product.
Currently, more and more people would like to share use experiences about a product on various shopping websites or social media (such as Microblog, Blog, virtual community, etc). Obviously, events (including activity, topic, etc.) related to a product mentioned in the description about use experiences can directly reflect the use scenarios of the product. FIG. 2A and FIG. 2B show examples of product reviews on a shopping website, wherein FIG. 2A shows the reviews for a water-proof camera, (b) shows the reviews for a mobile phone. According to the reviews in FIG. 2A, the information about one use scenario of the water-proof camera can be obtained, wherein “activity” is “photoing and shooting under water”, “opinion” is “very good, very gelivable” (positive), “location” is “Maldives”. Similarly, according to the reviews in FIG. 2A, the information about the use scenario of the mobile phone can be obtained, wherein “activity” is “give a present”, “opinion” is “likes it very much” (positive).
Therefore, many people will search for related contents on the Internet before purchasing a product. However, such contents are very huge, and it is time consuming to obtain related information. Moreover, different persons may use different words to express the same meaning. Thus, although the related information can be obtained, more useful information may not be obtained.
On the other hand, online recommendation gradually becomes one way for product advertising. The existing online recommendation method is based on a keyword, wherein the keyword is contained in the recommended results. For example, when a keyword “Maldives” is searched for online, the relevant recommendation links such as “Maldives hotels”, “Maldives air ticket”, “Maldives view spots” and the like will appear. Actually, in some cases, such a recommendation cannot meet a customer's demand, e.g., a customer wants to a recommendation of products for the use scenario of “Maldives”. Therefore, the existing online recommendation method cannot recommend the suitable products according to the use scenarios.
Therefore, it is desired to be capable of establishing an association between a product and a use scenario according to the description about use experiences of the product, categorizing the associations, and thus accurately recommending the product suitable for the use scenario required by the customer.